TY - JOUR

T1 - Contextual classification of point clouds using a two-stage CRF

AU - Niemeyer, J.

AU - Rottensteiner, F.

AU - Soergel, U.

AU - Heipke, C.

PY - 2015/3/10

Y1 - 2015/3/10

N2 - In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation.

AB - In this investigation, we address the task of airborne LiDAR point cloud labelling for urban areas by presenting a contextual classification methodology based on a Conditional Random Field (CRF). A two-stage CRF is set up: in a first step, a point-based CRF is applied. The resulting labellings are then used to generate a segmentation of the classified points using a Conditional Euclidean Clustering algorithm. This algorithm combines neighbouring points with the same object label into one segment. The second step comprises the classification of these segments, again with a CRF. As the number of the segments is much smaller than the number of points, it is computationally feasible to integrate long range interactions into this framework. Additionally, two different types of interactions are introduced: one for the local neighbourhood and another one operating on a coarser scale. This paper presents the entire processing chain. We show preliminary results achieved using the Vaihingen LiDAR dataset from the ISPRS Benchmark on Urban Classification and 3D Reconstruction, which consists of three test areas characterised by different and challenging conditions. The utilised classification features are described, and the advantages and remaining problems of our approach are discussed. We also compare our results to those generated by a point-based classification and show that a slight improvement is obtained with this first implementation.

KW - Classification

KW - Conditional Random Fields

KW - Contextual

KW - LiDAR

KW - Point cloud

KW - Urban

UR - http://www.scopus.com/inward/record.url?scp=84925347934&partnerID=8YFLogxK

U2 - 10.5194/isprsarchives-XL-3-W2-141-2015

DO - 10.5194/isprsarchives-XL-3-W2-141-2015

M3 - Conference article

AN - SCOPUS:84925347934

VL - 40

SP - 141

EP - 148

JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives

SN - 1682-1750

IS - 3W2

T2 - Joint ISPRS Conference on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015

Y2 - 25 March 2015 through 27 March 2015

ER -